Opposition to voluntary and mandated COVID-19 vaccination as a dynamic process: Evidence and policy implications of changing beliefs

Significance The challenge of securing adherence to public health policies is compounded when an emerging threat and a set of unprecedented remedies are not fully understood among the general public. The evolution of citizens’ attitudes toward vaccination during the COVID-19 pandemic offers psychologically and sociologically grounded insights that enrich the conventional incentives- and constraints-based approach to policy design. We thus contribute to a behavioral science of policy compliance during public health emergencies of the kind that we may increasingly face in the future. From early in the pandemic, we have tracked the same individuals, providing a lens into the conditions under which people’s attitudes toward voluntary and mandated vaccinations change, providing essential information for COVID-19 policy not available from cross-section data.

Tables S1 to S7 (see the next page) Table S1. Number of participants in the three cross-section waves of the survey and the panel, dropouts, and exclusion criteria. Table S2. Socio-demographics for the representative cross-sections and the panel waves. Table S3. Comparing opposition to vaccination with and without sample weights in the panel sample. Table S4. Explaining the variables in the regressions. Table S5. Goodness of fit predicting consistent opposition to vaccination and movement from opposition to agreement between waves 2 and 3. Table S6. Transition matrices and stationary distributions for the full 5-point Likert scores. Table S7. Transition matrices and stationary distributions for a 3-state process (opposed, undecided, and willing).

Main survey question on agreement with being vaccinated (original screenshot)
-Schools and day-care centers are already closed in most of the federal states, others will follow.
-Shops are to close -except for supermarkets, pharmacies, drugstores, petrol stations and hairdressers.
-Restaurants may open only between 6am and 6pm.
-Places of worship, playgrounds, sports facilities, bars, clubs, theatres, cinemas, concert halls and museums will be completely closed. -Restrictions on travel, borders are closed.
-Historic TV address of Chancellor Merkel. In an urgent appeal, she calls on the population to act in solidarity and responsibility. "Social contacts must be minimized." "This is serious. Take it seriously, too." -EU imposes entry ban.   -Federal and state governments agree on strict restrictions on exit and contact. Citizens may only be in public areas with a maximum of one person who does not live in the same household and must keep at least 1.5m distance. -Restaurants and pubs may only offer take-away food. Hairdressers must close.
28 March. Infection Protection Act comes into force (i.e., the government is entitled to restrict fundamental rights).
-The nationwide contact restrictions are extended until 19 April. People should generally refrain from private travel and visits -including those by relatives.
-The severe restrictions on contact will be extended until 3 May.
-Stepwise reopening of schools on May 4.
-Restaurants, bars and pubs are to remain closed as before.
-Major events will also remain prohibited until at least 31 August.
16 April. Government recommends to wear community masks when shopping and in public transport.
17 April. Germany survived the first wave of Covid-19 well, gradually returning to normality. -First test subjects of German vaccination study have been injected.
-Minister of Health Spahn: enforced vaccination will not be necessary, voluntary willingness to get vaccinated is sufficient.
30 April. Chancellor Merkel is consulting with the heads of the federal states on how to proceed: -Contact restrictions remain in force for the time being. Citizens are to keep a minimum distance of 1.5 meters in public and only stay there alone, with another person not living in the household or with members of their own household. -Playgrounds are to be permitted again under certain conditions. -Community worship services should be allowed again with rules on distance and hygiene.
-Schools and daycare centers: no changes, federal and state governments want to discuss this in more detail on 6 May. -Restaurants, hotels and cafés will remain closed.
-No changes for the time being with respect to store openings. 24 September. According to the German Standing Committee on Vaccination (STIKO), it is unclear whether enforcement of COVID-19 vaccinations will be beneficial. Someone who absolutely does not want to be vaccinated will always find ways to get around it.
-CureVac expects vaccine in the first half of 2021. Second phase of clinical trial started this week.
-Minister of Health Spahn: Prioritized vaccination may be needed, such that, for example, health care workers and at-risk groups get the vaccine first.
-AstraZeneca announces that it will provide vaccine data from trial series by the end of the year.
7 October. The U.S. Food and Drug Administration has tightened the requirements for emergency approval of a COVID-19 vaccine -apparently against the will of the White House.
-The number of infections in Germany is increasing dramatically and almost reaches the mark of 4,000 new cases in one day. Alarming thresholds are exceeded in Berlin and Frankfurt. -Research Minister Anja Karliczek expects widespread COVID-19 vaccination to be possible from mid-2021. Currently, three companies are receiving federal funding for vaccine development.
10 October. Although Chinese vaccine candidates are still in final testing phase, hundreds of thousands of Chinese are already being vaccinated. Still unclear how safe and effective Chinese vaccines actually are.
-One district exceeds the critical mark of 50 new infections per 100,000 inhabitants within seven days. -Federal Health Minister Spahn expects that vaccinations in Germany can begin in the first quarter of 2021. People with pre-existing health conditions, the elderly and health and care workers will be targeted first.
14 October. The federal and state governments agree on new containment policies in hotspots: -In regions with 50 or more new infections per 100,000 inhabitants within seven days, private parties are to be limited to a maximum of ten people and two households. There is to be an 11 p.m. curfew for restaurants. -In regions with 35 new infections per 100,000 inhabitants within seven days, the mask requirement is also to apply where people gather more closely or for longer periods.
-The number of new infections in Germany rises to over 6,000 within a day.
-Reactions to the federal-state resolutions on infection control are polarized.
-Bavaria issues its own, stricter regulations.
-Courts in two federal states overturn a controversial ban on accommodation for guests from highrisk regions within Germany, while other state governments suspend it.
-In view of the sharp rise in new infections, Chancellor Merkel is calling on the population to reduce contacts as far as possible. -BioNTech/Pfizer has already started mass production of a vaccine.
-Pfizer will apply for emergency approval in the U.S. by the end of November.
13-18 October. Reports of fake news claiming that vaccination would be compulsory and fact checks that this is not true.   26 October. AstraZeneca's vaccine appears to elicit a "robust immune response" among the elderly. Results will be published soon.
SURVEY WAVE 2 STARTS ON 28 OCTOBER, 2020 28 October. Second lockdown "light" announced in Germany, lasting for the entire month of November: -Restaurants and houses of culture close, no tourism.  -German Standing Committee on Vaccination (STIKO) announces that Germany will not be fully vaccinated (which meant vaccinated twice at that time) until 2022. -According to an article in The Lancet by the chair of the U.K. Vaccine Task Force, upcoming vaccines "probably won't be perfect" and "may not work for everyone".
30 October. EU spreads optimism, is the vaccine coming soon? European Parliament is discussing December/January as start dates for vaccinations. Health professionals are to be vaccinated first. But the first vaccines may not fully protect everyone.
31 October. Paul Ehrlich Institute expects first vaccine approvals in early 2021. However, the approval of a vaccine does not mean that it will be immediately available to the entire population.
-A suggestion for the national vaccination strategy will be presented in the coming week. It will address an ethically sensitive question: who comes first, who comes last? -Nearly one in ten German health departments complains of being overwhelmed.
-The vaccine of the Germany-based company CureVac has successfully passed the first clinical test phase. The 250 subjects showed a responsive immune response and good tolerability.

November.
Despite tight contact restrictions, COVID-19 infections in Europe continue to rise rapidly.
-German government classifies almost all of Europe as a risk area. Hospitals in Germany are preparing contingency plans for the treatment of Covid-19 patients. -Mutation of SARS-CoV-2 found on a mink farm in Denmark, concern that the vaccines developed so far might not work against mutations.
-BioNTech/Pfizer announce that a vaccine will be available soon, 90% success rate. -Chancellor Merkel announces that the lockdown policies will become more stringent instead of relaxed. 11 February. The federal and state governments have not been able to agree on a detailed stage-by-stage plan on openings, but at least they have agreed on a first concrete threshold for a gradual ramp-up of the economy and for openings.
22 February. Swedish regions stop vaccination with Astrazeneca -"Surprising" accumulation of side effects.
March 2021 4 March. The lockdown will remain in place until March 28, but with some relaxations. Depending on the incidence, there will be further relaxations, or an emergency brake. The German parliament has passed the nationwide COVID-19 "emergency brake". The federal government can thus significantly expand its powers.
29 April. Israel investigates cases of heart muscle inflammation after vaccination.  -According to Federal Health Minister Spahn, the third wave of the pandemic seems to be broken. However, the case rates in Germany remain at a high level and must decrease. Prioritization of AstraZeneca's vaccine will now be completely abandoned.

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Horrible pictures from India given the new variant.
9 May. New regulations in force: relief for vaccinated and convalescent patients.
-One third of Germans vaccinated at least once. -Anti-COVID-19 emergency break could expire at the end of June. CDU/CSU and SPD are considering not extending the central policies to contain the pandemic. In the event of another wave, the federal states would then be responsible again.
-First vaccine for children approved in the EU (Biontech/Pfizer).

June 2021
1 June. New study suggests that the positive effects of lockdown and emergency brake are significantly overestimated.
10 June. STIKO recommends vaccination for 12 to 17 years old children with pre-existing health issues.
17 June. CureVac vaccine not as effective as hoped for (efficacy of 47 percent).
26 June. The Delta variant is now most common in Germany. Vaccines protect less against this variant.
12 July. Member of the Ethics council calls for mandatory vaccination of teachers and educators.
-Several federal states are beginning to implement the stricter policies. In doing so, some are going beyond the resolutions of the federal government and the state premiers.

Evidence that the observed small share of those consistently opposed to vaccination is not due to response error
Response error (random mistakes in recording one's answer to a survey item) may generate inconsistency across the waves of the panel, falsely appearing to document movement out of and into opposition to vaccination. We showed in the text that from our data we can infer that even if there were to have been no change in vaccination attitudes, so that error is the only source of inconsistency in our data, we would still find a very small share of (by assumption consistent) opponents to voluntary vaccination. Our data give us the following additional reasons to think that it is unlikely that the small fraction that we observe to be consistently opposed to vaccination could be primarily the result of response error.

1.
Those favoring vaccinations are much less inconsistent. If lack of consistency among those opposed to voluntary vaccinations were due to response noise, then we would expect to observe similar lack of consistency among those favoring vaccinations. As is evident in Fig. 2 in the paper, this is not the case, as can also be seen from Fig. S1 below. The latter figure presents individuals' level of agreement with being vaccinationed in the next survey wave depending on their level of agreement in the previous wave. The black bars refer to the consistent respondents (those who responded the same way in two consecutive waves). For example, the top panel captures those strongly opposed to vaccination (agreement level 0) in wave 1 (left) and wave 2 (right). Only 37 percent of them are consistent in the second and 38 percent are consistent in the third wave. In contrast, among strong supporters (level 4 in the previous wave, bottom panel), 63 percent (left) and 75 percent (right) were consistent in the next wave.
Another look at the evidence: The within-individual variance of responses over the 3 waves is 2.34 for those who strongly opposed voluntary vaccinations in at least one of the waves and only 0.97 for those who strongly favored vaccinations at least once.
If one were to arbitrarily assign different response error rates to different types of attitudes towards vaccination, one could probably reconcile our survey results with the hypothesis that there is no inconsistency among those opposed and that there are a great many of them. But we doubt that there is a plausible account of the source of error in responses that would explain the difference in consistency of the opposed and those supporting vaccinations. The same applies to the further evidence below.
2. Reported changes in vaccination attitudes are associated with plausible changes in beliefs. We find the statistical associations shown in Fig. 3B and Fig. S11, for wave 2 and wave 3 (for which we have adequate data on changes in beliefs), to be plausible, suggesting that those changing their response from one wave to the next have likely changed their attitude towards vaccination rather than recorded their beliefs erroneously.

Opposition to mandated vaccinations is much less inconsistent.
If response noise were the reason for the small number of consistently opposed (responding 0 or 1) to voluntary vaccinations then we would expect to find a similar noise-induced appearance of inconsistency in other parts of the survey, e.g. for the case in which vaccinations are required by law. But this is not what we find. The persistent 3.32 percent represent a fifth (19.64%) of the average opposition to voluntary vaccination (3.32% persistent across the three waves divided by 16.90%, the average level of opposition over the three waves, equals 19.64%). By contrast, in case of enforcement, half of the average opposition is persistent across the three waves (16.50% persistent opposition divided by 33.17% average opposition equals 49.75%). Thus, the volatility of opposition is peculiar to the voluntary case, which would be unlikely if the observed inconsistency were largely noise. 4. Those undecided change asymmetrically towards favoring vaccination, rather than randomly. We can also make inferences based on those who were undecided (responding with a 2 on the Likert scale that ranges from 0 to 4) in an earlier wave and responded differently in a later wave. If inconsistency of those opposed to voluntary vaccination were mainly due to noise, we would expect their later responses to be distributed symmetrically (equal numbers changing to oppose and to favor). But as the third row of Fig. S1 shows, when the undecided change, they do so asymmetrically, overwhelmingly switching to agreement (between the first and second waves 3 times more switch to favoring than to opposing; between the second and third waves 5.4 times more switch toward favoring).

Our survey predicts the subsequent level of vaccinations.
If the data were very noisy it would be unlikely that the fraction reporting willingness to be vaccinated at the beginning of the vaccination campaign (May 2021) would so accurately predict those who were actually vaccinated in the succeeding two months (vaccines became widely available only in summer) and the slowdown in vaccinations after reaching that level.
We conclude from these 5 points (and from the hypothetical example in the paper) that it is unlikely that the apparent inconsistency of those who record opposition to voluntary vaccination could be primarily the result of response noise.

Vaccination rate for adult population based on the vaccination rate for the entire population in Germany and its evolution over time.
Fig . S3 shows the percentage of the total population vaccinated with at least one dose. This number grew at the average rate of 1.37% per day between the midpoint of our May 2021 survey (May 11 th ) wave and July 23 th when the estimated fraction of adults vaccinateed reached 73%, and at the rate of 0.16% daily between then and November 18 th when vaccination mandates for health care workers were announced in Germany (see the timeline). In Germany, 13.75 million are younger than 18 (3), which corresponds to 16.5% of the total population of 83.1 million. The 61% vaccination rate based on the total population end of July (2) then corresponds to 73% of the adult population (0.61/(1 -0.165) = 0.73). Fig. S4 shows the cumulative distribution of the willingness to be vaccinated (excluding those few vaccinated twice). For example, the blue and red lines show that 55% and 36%, respectively, of respondents "fully agreed" with being vaccinated if it is voluntary and enforced, respectively. Opposition (levels 0 and 1) was expressed by 15% if voluntary and 35% if enforced (that is, the final two steps in the graph).  Fig. 2 in the paper, showing that from one wave to the next most of the undecided became willing in the case of voluntary vaccinations (left). In the case of enforcement (right), a substantial fraction of the undecided became opposed from wave 1 to wave 2, while the majority of the undecided became willing from wave 2 to wave 3.
Most of the undecided changed their minds, 65 percent switching to willing from the second to the third wave and just 12 percent becoming unwilling if vaccinations are voluntary. In case of enforcement, the dynamics of the undecided have changed. Earlier in the pandemic, 46 percent of the undecided subsequently became opposed and only 29 percent changed their mind towards willingness from wave 1 to wave 2. But from wave 2 to wave 3, 55 percent of the previously undecided became willing and 20 percent became opposed.

If voluntary: Those undecided in previous wave
If enforced: Those undecided in previous wave

A note on the representativeness (on vaccination attitudes) of the German panel and a comparison with U.S. cross-section data.
Fig . S6 provides information about the extent to which our German 3-wave panel is representative of the German population (comparing the first and second row) with respect to their vaccination attitudes and how different the German population sampled is from the Kaiser Family Foundation cross-section sample in the U.S. (comparing the second and third row). We cannot compare German and U.S. populations on changes in individual's vaccination preferences because no U.S. data equivalent to our survey exist.
Comparing the upper two rows in Fig. S6 shows that the distribution of responses is very similar, suggesting that our panel is not unrepresentative of the German population in this respect. The second comparison (second and third rows) shows that the German public in May 2021 differed from the U.S. in March 2021 (somewhat fewer already vaccinated, more "wait and see"), but the differences are not substantial. Using the numbers from Fig. S6, as a fraction of those unvaccinated, those who wanted to "wait and see" were 0.25 in the U.S. and 0.18 Germany, those "definitely not" getting vaccinated 0.19 and 0.17, respectively, and those who would get vaccinated "only if required" 0.10 and 0.08, respectively. We selected this KFF survey because the distibution of vaccination attitudes among those remaining unvaccinated depends on the fraction already vaccinated (all of whom must have been willing). By the second half of April when the next KFF survey was conducted, the fraction already vaccinated had risen to 56 percent. We could not ask the KFF questions in the two earlier waves of our panel, because the KFF survey started only in December 2020. The KFF data are available under reference (4).

Recalculating our main results using separate samples of older East and West Germans
Below we compare the East and West German older cohorts (i.e., born before 1970, who had therefore reached adulthood before the end of the GDR) with respect to: • the fraction who are consistently opposed to voluntary vaccination; • the extent to which being consistently opposed is predicted by socio-demographic characteristics alone and by the full model including beliefs; • and the fractions agreeing with and opposed to being vaccinated in at least one of the three waves.
We show that in all cases, results computed using the samples of older East and West Germans are very similar.
In Fig. S7, concerning our key finding, we show that the fraction consistently opposed to voluntary and enforced vaccination (responding 0 or 1 in all waves, orange slices) is very similar among older East and West Germans both if vaccinations are voluntary and enforced. Turning to our second key finding, Fig. S8 shows that for older East and West Germans alike, socioeconomic variables alone do not predict consistent opposition to voluntary vaccination (Tjur's R 2 : East 0.015; West 0.009), while adding beliefs predicts substantial shares of the differences between those consistently opposed and others (East: 0.156; West: 0.212). A similar pattern holds for enforced vaccinations.

Fig. S8: The fraction of difference between the consistently opposed and others explained by sociodemographic differences alone and the full model including beliefs (Tjur's R 2 ).
Sample sizes: n=357 for older East Germans and n= 851 for older West Germans.
Finally, we ask whether older East and West Germans differ in the fractions that were opposed to and agreed with being vaccinated, respectively, in at least one of our three waves. Again, East and West Germans do not differ in any appreciable way, as shown in Fig. S9.   Table S5. 3 (levels 3 or 4). Shown are the coefficients and 95% CI, estimated with normalized variables except for dummy variables. The level variables in attitudes and beliefs here all refer to wave 2. The Δ variables reflect the difference wave 3wave 2 and should be read as "increase in…". We could not perform the same analyzes for switching between waves 1 and 2 because we elicited the relevant attitudes and beliefs only from wave 2 on.

Fig. S12. Agreement with being vaccinated if voluntary and if enforced in within-subjects and between-subjects designs.
In the between-subjects case in which there cannot be a demand effect, enforcement substantially reduces agreement. Thus, our evidence that a mandate induces a control averse response is not due to a demand effect of the within-subjects design. These treatments were implemented using a representative non-panel sample in wave 2 (within-subjects: n=245; between-subjects, voluntary: n=215; between-subjects, enforced: n=229).

Table S1. Number of participants in the three cross-section waves of the survey and the panel, dropouts and exclusion criteria.
Exclusions according to the shown criteria were performed by the surveyLab, based on an independent quality check in which the authors of this paper were not involved.
The majority of those in the second row of the table were eliminated by the survey algorithm designed to ensure a representative sample (the appropriate shares of particular combinations of sociodemographics were required for representativeness, e.g., low educated young females from a given region).

Cross-section surveys
Wave Attitudes towards vaccination are very similar between the panel and the cross-section surveys, as are the vaccination status in the third wave, the level of education, and the fraction of East Germans. Support for the right-wing, anti-government party AfD is very similar in our panel and our cross-section surveys to others conducting regular surveys on political party preferences. The panel is somewhat older and more male than the cross-sections. But Figs. S10 and S11 show that neither age nor gender is an important predictor of our variables of interest. Similarly, Table S3 shows that using population sample weights on the panel gives similar estimates of opposition to vaccination.  Table S4. Explaining the variables in the regressions. Note that in the regression models predicting consistent opposition (Fig. 3A in the paper and Fig. S10), we use the average of the beliefs and attitudes in the three survey waves (or the average of the second and third waves for questions which were not asked in the first wave). To predict moving from opposition to vaccination in wave 2 towards willingness in wave 3 (Fig. 3B in the paper and Fig. S11), we use the wave 2 beliefs and attitudes as the level variables. Therefore, we indicate the distributions for both cases. Q1 (Q2 and Q3) refer to the 1 st quartile (median and 3 rd quartile) of the distributions. In all regressions, sociodemographic variables which are dummies were used from wave 2, and continuous variables are averaged over the 3 survey waves (e.g., income).

Trust in public institutions
Average of 4 measures of trust in public institutions: trust in federal government, trust in state government, trust in science, and trust in media.

COVID-19 critical locally
Survey question included only in waves 2 and 3.
How critical do you think the COVID-19 situation currently is in your region? (9-point Likert scale ranging from 1 "not critical at all" to 9 "highly critical")

Predicting consistent opposition and movement out of opposition to vaccinations.
In Table S5 and Figs. S10 and S11 we present information underlying Fig. 3 of the paper and the passage surrounding it. We estimate both logit and linear probability models (the former presented in the paper). Because the mean of the dependent variable is close to zero for predicting consistent opposition (very few consistently opposed), a large fraction of the predicted values lie outside the unit interval when we implement the linear probability model, so that, following the evidence in Horace and Oaxaca (8), we use the logit to present our main findings and complement them with linear probability models for a more intuitive interpretation of the coefficients.
Table S5 provides evidence that for both the logit and linear probability models socio-demographic variables alone (including the indirect effects of their covariation with beliefs and attitudes) account for far less of the differences than the full model in which attitudes and beliefs are included. In Fig. S10 (predicting consistent opposition to vaccinations) the only socio-demographic variable of any importance is (for the case of enforced vaccinations) the dummy variable indicating that the respondent spent their childhood in the territory of the former GDR. This is consistent with the fact that East Germans, especially those raised under Communist Party rule, have been found to be less control averse (9,10). In Fig. S11 (predicting switching from opposed to favoring vaccinations) this holds to a lesser extent. Nonetheless, Tjur's R 2 in Table S5 shows that sociodemographic measures, taken as a whole, have very little explanatory power. A more complete presentation of our evidence on these dynamics is afforded by the transition matrices 12 P and 23 P recording changes in attitudes between the first and second, as well as the second and third waves of the survey respectively. The corresponding elements are , 1 t t ij p + , the probability that an individual responding with Likert scale score i in the earlier wave t responds with a score j in the later wave t+1 where ,

Transition matrices and stationary distributions
[0,1, 2,3, 4] i j ∈ . We present the wave 1 to wave 2 and wave 2 to wave 3 P matrices for the cases of mandated and voluntary vaccination in Table S6. The stationary distribution * λ is defined by * * , P λ λ = that is, the λ that solves: To be clear, the stationary distribution is not a measure of consistent opposition in the sense we have described it in the paper, namely the extent to which individuals express unchanging vaccination attitudes over time (these are the diagonal elements in the P matrix, ii p ). Instead, the λ vector is the distribution that would persist in the long run were P not to change, with those individuals making up the five fractions of the population changing from period to period, the distribution itself persisting but not the membership of its states. Table S6 provides additional information on the transience of opposition. Panel A shows, for example, that of those strongly disagreeing with being vaccinated voluntarily in wave 1, only 46 percent (that is 37 percent responding 0 plus 9 percent responding 1) disagreed (either weakly or strongly) in wave 2. The stationary distributions appear in bold below the P matrices in Table S6. Table S7 presents reduced transition matrices and associated stationary distributions with just three states: vaccine willingness, vaccine opposition and undecided. We can see that opposition to voluntary vaccination in the stationary distribution is somewhat less in the wave 2 to wave 3 P matrix than in the wave 1 to wave 2 P matrix (11 percent rather than 18 percent). For the case of enforced vaccinations, there is a very substantial drop in the weight of vaccine opposition in the stationary distribution, from over half based on the first and second waves to a quarter of the sample based on the second and third waves. Panel E includes the 7 percent who were vaccinated twice in the 'willing' category for the case of voluntary vaccination and shows that the inclusion of those vaccinated twice does not qualitatively change the conclusions one would draw from the transition matrix and the associated stationary distribution. Table S6. Transition matrices and stationary distributions for the full 5-point Likert scores. The transition matrices including the third wave (panels C, D, E and F) are based on the 93 percent of the sample that was not vaccinated twice in May 2021. Table S7. Transition matrices and stationary distributions for a 3-state Markov process (opposed, undecided, and willing). This simpler transition matrix treats as single states opposition (either strong or weak, Likert scale answers 0 or 1) and willingness (either weak or strong, Likert scale answers 3 or 4) with undecided as the third, intermediate state (Likert scale answer 2). As done for Table S6, the matrices including the third wave (panels C, D, F and G) include the 93 percent that were not vaccinated twice in May 2021. Panels E and H include the 7 percent who were vaccinated twice in the 'willing' category for the case of voluntary vaccination and show that the inclusion of those vaccinated twice does not qualitatively change the conclusions one would draw from the transition matrix and the associated stationary distribution. Transience is also evident from the self-reported behavioral data: Among those opposed to voluntary vaccination in wave 2, 38.5 percent were vaccinated at least once or had an appointment in wave 3. Among the undecided in wave 2, 44.6 percent were vaccinated at least once or had an appointment in wave 3, and among those willing if vaccinations are voluntary in wave 2, 51.0 percent were vaccinated at least once or had an appointment in wave 3 (remember that vaccine availability as well as appointments were very limited at the time of our survey wave 3).